When the amount of data and information is said to double in every 20 months or so, feature
selection has become highly important and beneficial. Further improvements in feature …

Feature selection aims to find the most important information to save computational efforts
and data storage. We formulated this task as a combinatorial optimization problem since the …

The rapid development of information techniques has lead to more and more high-
dimensional datasets, making classification more difficult. However, not all of the features …

The selection feature is an important and fundamental step in the preprocessing of many
classification and machine learning problems. The feature selection (FS) method is used to …

Feature selection can efficiently improve classification accuracy and reduce the dimension
of datasets. However, feature selection is a challenging and complex task that requires a …

Feature selection methods are deployed in machine-learning algorithms for reducing the
redundancy in the dataset and to increase the clarity in the system models without loss of …

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial
Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has …

Feature selection aims to find the most important information from a given set of features. As
this task can be seen as an optimization problem, the combinatorial growth of the possible …

Background: Feature selection sometimes also known as attribute subset selection is a
process in which optimal subset of features are elected with respect to target data by …

Data classification is a challenging problem. Data classification is very sensitive to the noise
and high dimensionality of the data. Being able to reduce the model complexity can help to …